Overview

Brought to you by YData

Dataset statistics

Number of variables44
Number of observations141250
Missing cells443060
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory270.7 MiB
Average record size in memory2.0 KiB

Variable types

Categorical18
DateTime4
Text9
Numeric8
Unsupported2
Boolean3

Alerts

GW_Transaction_Status has constant value "settled" Constant
GW_Response has constant value "Approved" Constant
GW_Trx_Type has constant value "payment" Constant
MP_Trx_Type has constant value "payment" Constant
Bank_Debits has constant value "0.0" Constant
BS_Amount is highly overall correlated with BS_GW_Match and 8 other fieldsHigh correlation
BS_Card_Type is highly overall correlated with BS_Payment_Gateway and 5 other fieldsHigh correlation
BS_Currency is highly overall correlated with BS_Foreign_Exchange_Rate and 3 other fieldsHigh correlation
BS_Foreign_Exchange_Rate is highly overall correlated with BS_Currency and 3 other fieldsHigh correlation
BS_GW_Match is highly overall correlated with BS_Amount and 16 other fieldsHigh correlation
BS_Payment_Gateway is highly overall correlated with BS_Card_Type and 7 other fieldsHigh correlation
BS_Payment_Method_Type is highly overall correlated with BS_Card_Type and 7 other fieldsHigh correlation
BS_Processor_Response_Message is highly overall correlated with BS_Amount and 10 other fieldsHigh correlation
BS_Record_Type is highly overall correlated with BS_Amount and 10 other fieldsHigh correlation
Bank_Credits is highly overall correlated with BS_GW_Match and 7 other fieldsHigh correlation
Bank_Deposit_Currency is highly overall correlated with BS_Currency and 8 other fieldsHigh correlation
GW_Card_Brand is highly overall correlated with BS_Card_Type and 6 other fieldsHigh correlation
GW_Net_Settled_Amt is highly overall correlated with BS_Amount and 4 other fieldsHigh correlation
GW_Transaction_Amount is highly overall correlated with BS_Amount and 4 other fieldsHigh correlation
GW_Trx_Currency is highly overall correlated with BS_Currency and 4 other fieldsHigh correlation
GW_fees is highly overall correlated with BS_Amount and 8 other fieldsHigh correlation
GW_name is highly overall correlated with BS_Card_Type and 8 other fieldsHigh correlation
MP_Processor_Fee is highly overall correlated with BS_Card_Type and 10 other fieldsHigh correlation
MP_Processor_Type is highly overall correlated with BS_Card_Type and 9 other fieldsHigh correlation
MP_Settled_Amount is highly overall correlated with BS_Amount and 7 other fieldsHigh correlation
MP_Trx_Currency is highly overall correlated with BS_Currency and 7 other fieldsHigh correlation
PG_MP_Match is highly overall correlated with BS_Amount and 10 other fieldsHigh correlation
end_to_end_recon_match is highly overall correlated with BS_Amount and 6 other fieldsHigh correlation
BS_Record_Type is highly imbalanced (68.6%) Imbalance
BS_Processor_Response_Message is highly imbalanced (68.6%) Imbalance
BS_GW_Match is highly imbalanced (51.2%) Imbalance
GW_Transaction_ID has 15000 (10.6%) missing values Missing
GW_name has 15000 (10.6%) missing values Missing
GW_Transaction_Status has 15000 (10.6%) missing values Missing
GW_Response_Code has 15000 (10.6%) missing values Missing
GW_Response has 15000 (10.6%) missing values Missing
GW_Transaction_Amount has 15000 (10.6%) missing values Missing
GW_Trx_Currency has 15000 (10.6%) missing values Missing
GW_Trx_Type has 15000 (10.6%) missing values Missing
GW_Card_Brand has 15000 (10.6%) missing values Missing
GW_fees has 15000 (10.6%) missing values Missing
GW_Net_Settled_Amt has 15000 (10.6%) missing values Missing
GW_Trx_Date has 15000 (10.6%) missing values Missing
GW_Ref_Number has 15000 (10.6%) missing values Missing
MP_Trx_ID has 16250 (11.5%) missing values Missing
MP_Processor_Type has 16250 (11.5%) missing values Missing
MP_Trx_Type has 16250 (11.5%) missing values Missing
MP_Processor_Fee has 16250 (11.5%) missing values Missing
MP_Settled_Amount has 16250 (11.5%) missing values Missing
MP_Settlement_Date has 16250 (11.5%) missing values Missing
MP_Settlement_Batch has 16250 (11.5%) missing values Missing
MP_Trx_Currency has 16250 (11.5%) missing values Missing
MP_Order_ID has 16250 (11.5%) missing values Missing
Bank_Deposit_Date has 20362 (14.4%) missing values Missing
Bank_Deposit_Currency has 20362 (14.4%) missing values Missing
Bank_Deposit_Ref_Number has 20362 (14.4%) missing values Missing
Bank_Credits has 20362 (14.4%) missing values Missing
Bank_Debits has 20362 (14.4%) missing values Missing
BS_Processor_Response_Code is an unsupported type, check if it needs cleaning or further analysis Unsupported
GW_Response_Code is an unsupported type, check if it needs cleaning or further analysis Unsupported
MP_Processor_Fee has 83229 (58.9%) zeros Zeros

Reproduction

Analysis started2025-07-06 23:53:51.070235
Analysis finished2025-07-06 23:54:05.118796
Duration14.05 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

BS_Record_Type
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
payment
125000 
refund
 
12500
chargeback
 
2500
chargeback_reversal
 
1250

Length

Max length19
Median length7
Mean length7.0707965
Min length6

Characters and Unicode

Total characters998750
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpayment
2nd rowpayment
3rd rowpayment
4th rowpayment
5th rowpayment

Common Values

ValueCountFrequency (%)
payment 125000
88.5%
refund 12500
 
8.8%
chargeback 2500
 
1.8%
chargeback_reversal 1250
 
0.9%

Length

2025-07-06T16:54:05.146324image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:54:05.172886image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
payment 125000
88.5%
refund 12500
 
8.8%
chargeback 2500
 
1.8%
chargeback_reversal 1250
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 143750
14.4%
n 137500
13.8%
a 133750
13.4%
p 125000
12.5%
y 125000
12.5%
m 125000
12.5%
t 125000
12.5%
r 18750
 
1.9%
u 12500
 
1.3%
d 12500
 
1.3%
Other values (10) 40000
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 998750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 143750
14.4%
n 137500
13.8%
a 133750
13.4%
p 125000
12.5%
y 125000
12.5%
m 125000
12.5%
t 125000
12.5%
r 18750
 
1.9%
u 12500
 
1.3%
d 12500
 
1.3%
Other values (10) 40000
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 998750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 143750
14.4%
n 137500
13.8%
a 133750
13.4%
p 125000
12.5%
y 125000
12.5%
m 125000
12.5%
t 125000
12.5%
r 18750
 
1.9%
u 12500
 
1.3%
d 12500
 
1.3%
Other values (10) 40000
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 998750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 143750
14.4%
n 137500
13.8%
a 133750
13.4%
p 125000
12.5%
y 125000
12.5%
m 125000
12.5%
t 125000
12.5%
r 18750
 
1.9%
u 12500
 
1.3%
d 12500
 
1.3%
Other values (10) 40000
 
4.0%
Distinct90
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2025-01-01 00:00:00
Maximum2025-03-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-06T16:54:05.208793image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:05.255961image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

BS_Currency
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
USD
84784 
EUR
21002 
GBP
14100 
AUD
11416 
CAD
9948 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters423750
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowGBP
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 84784
60.0%
EUR 21002
 
14.9%
GBP 14100
 
10.0%
AUD 11416
 
8.1%
CAD 9948
 
7.0%

Length

2025-07-06T16:54:05.297455image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:54:05.322372image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
usd 84784
60.0%
eur 21002
 
14.9%
gbp 14100
 
10.0%
aud 11416
 
8.1%
cad 9948
 
7.0%

Most occurring characters

ValueCountFrequency (%)
U 117202
27.7%
D 106148
25.0%
S 84784
20.0%
A 21364
 
5.0%
E 21002
 
5.0%
R 21002
 
5.0%
G 14100
 
3.3%
B 14100
 
3.3%
P 14100
 
3.3%
C 9948
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 423750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 117202
27.7%
D 106148
25.0%
S 84784
20.0%
A 21364
 
5.0%
E 21002
 
5.0%
R 21002
 
5.0%
G 14100
 
3.3%
B 14100
 
3.3%
P 14100
 
3.3%
C 9948
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 423750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 117202
27.7%
D 106148
25.0%
S 84784
20.0%
A 21364
 
5.0%
E 21002
 
5.0%
R 21002
 
5.0%
G 14100
 
3.3%
B 14100
 
3.3%
P 14100
 
3.3%
C 9948
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 423750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 117202
27.7%
D 106148
25.0%
S 84784
20.0%
A 21364
 
5.0%
E 21002
 
5.0%
R 21002
 
5.0%
G 14100
 
3.3%
B 14100
 
3.3%
P 14100
 
3.3%
C 9948
 
2.3%
Distinct45195
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Memory size7.8 MiB
2025-07-06T16:54:05.466007image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.8150584
Min length8

Characters and Unicode

Total characters1245127
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8795 ?
Unique (%)6.2%

Sample

1st rowCUST15159
2nd rowCUST20779
3rd rowCUST28404
4th rowCUST7007
5th rowCUST39746
ValueCountFrequency (%)
cust30007 16
 
< 0.1%
cust20916 15
 
< 0.1%
cust49463 15
 
< 0.1%
cust1260 15
 
< 0.1%
cust22216 13
 
< 0.1%
cust9060 13
 
< 0.1%
cust16580 13
 
< 0.1%
cust44259 13
 
< 0.1%
cust8676 13
 
< 0.1%
cust8906 13
 
< 0.1%
Other values (45185) 141111
99.9%
2025-07-06T16:54:05.642467image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 141250
11.3%
U 141250
11.3%
S 141250
11.3%
T 141250
11.3%
4 86333
 
6.9%
3 85701
 
6.9%
1 85340
 
6.9%
2 85096
 
6.8%
8 57083
 
4.6%
9 56870
 
4.6%
Other values (4) 223704
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1245127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 141250
11.3%
U 141250
11.3%
S 141250
11.3%
T 141250
11.3%
4 86333
 
6.9%
3 85701
 
6.9%
1 85340
 
6.9%
2 85096
 
6.8%
8 57083
 
4.6%
9 56870
 
4.6%
Other values (4) 223704
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1245127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 141250
11.3%
U 141250
11.3%
S 141250
11.3%
T 141250
11.3%
4 86333
 
6.9%
3 85701
 
6.9%
1 85340
 
6.9%
2 85096
 
6.8%
8 57083
 
4.6%
9 56870
 
4.6%
Other values (4) 223704
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1245127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 141250
11.3%
U 141250
11.3%
S 141250
11.3%
T 141250
11.3%
4 86333
 
6.9%
3 85701
 
6.9%
1 85340
 
6.9%
2 85096
 
6.8%
8 57083
 
4.6%
9 56870
 
4.6%
Other values (4) 223704
18.0%

BS_Amount
Real number (ℝ)

High correlation 

Distinct122801
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1979.2208
Minimum-4999.85
Maximum4999.87
Zeros0
Zeros (%)0.0%
Negative15000
Negative (%)10.6%
Memory size1.1 MiB
2025-07-06T16:54:05.681270image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-4999.85
5-th percentile-2655.288
Q1828.465
median2221.17
Q33607.4425
95-th percentile4724.491
Maximum4999.87
Range9999.72
Interquartile range (IQR)2778.9775

Descriptive statistics

Standard deviation2112.685
Coefficient of variation (CV)1.0674327
Kurtosis1.0466637
Mean1979.2208
Median Absolute Deviation (MAD)1389.74
Skewness-1.0062879
Sum2.7956493 × 108
Variance4463437.9
MonotonicityNot monotonic
2025-07-06T16:54:05.723644image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4756.12 5
 
< 0.1%
953.97 5
 
< 0.1%
931.3 5
 
< 0.1%
3543.24 5
 
< 0.1%
4146.9 5
 
< 0.1%
1020.37 5
 
< 0.1%
1902 5
 
< 0.1%
2595.9 5
 
< 0.1%
1121 4
 
< 0.1%
4540.24 4
 
< 0.1%
Other values (122791) 141202
> 99.9%
ValueCountFrequency (%)
-4999.85 1
< 0.1%
-4999.06 1
< 0.1%
-4998.84 2
< 0.1%
-4998.76 1
< 0.1%
-4998.23 1
< 0.1%
-4996.55 2
< 0.1%
-4996.24 1
< 0.1%
-4996.23 2
< 0.1%
-4996.21 2
< 0.1%
-4995.93 1
< 0.1%
ValueCountFrequency (%)
4999.87 1
< 0.1%
4999.85 2
< 0.1%
4999.66 1
< 0.1%
4999.61 1
< 0.1%
4999.54 1
< 0.1%
4999.44 1
< 0.1%
4999.28 2
< 0.1%
4999.27 1
< 0.1%
4999.26 1
< 0.1%
4999.24 1
< 0.1%

BS_Payment_Method_Type
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
Credit Card
47296 
PayPal
47110 
ACH
46844 

Length

Max length11
Median length6
Mean length6.6792779
Min length3

Characters and Unicode

Total characters943448
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPayPal
2nd rowACH
3rd rowACH
4th rowCredit Card
5th rowPayPal

Common Values

ValueCountFrequency (%)
Credit Card 47296
33.5%
PayPal 47110
33.4%
ACH 46844
33.2%

Length

2025-07-06T16:54:05.762086image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:54:05.784290image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
credit 47296
25.1%
card 47296
25.1%
paypal 47110
25.0%
ach 46844
24.8%

Most occurring characters

ValueCountFrequency (%)
a 141516
15.0%
C 141436
15.0%
r 94592
10.0%
d 94592
10.0%
P 94220
10.0%
e 47296
 
5.0%
i 47296
 
5.0%
t 47296
 
5.0%
47296
 
5.0%
y 47110
 
5.0%
Other values (3) 140798
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 943448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 141516
15.0%
C 141436
15.0%
r 94592
10.0%
d 94592
10.0%
P 94220
10.0%
e 47296
 
5.0%
i 47296
 
5.0%
t 47296
 
5.0%
47296
 
5.0%
y 47110
 
5.0%
Other values (3) 140798
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 943448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 141516
15.0%
C 141436
15.0%
r 94592
10.0%
d 94592
10.0%
P 94220
10.0%
e 47296
 
5.0%
i 47296
 
5.0%
t 47296
 
5.0%
47296
 
5.0%
y 47110
 
5.0%
Other values (3) 140798
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 943448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 141516
15.0%
C 141436
15.0%
r 94592
10.0%
d 94592
10.0%
P 94220
10.0%
e 47296
 
5.0%
i 47296
 
5.0%
t 47296
 
5.0%
47296
 
5.0%
y 47110
 
5.0%
Other values (3) 140798
14.9%

BS_Card_Type
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.3 MiB
PayPal
47110 
ACH
46844 
Mastercard
15960 
Visa
15697 
Amex
15639 

Length

Max length10
Median length6
Mean length5.0133522
Min length3

Characters and Unicode

Total characters708136
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPayPal
2nd rowACH
3rd rowACH
4th rowVisa
5th rowPayPal

Common Values

ValueCountFrequency (%)
PayPal 47110
33.4%
ACH 46844
33.2%
Mastercard 15960
 
11.3%
Visa 15697
 
11.1%
Amex 15639
 
11.1%

Length

2025-07-06T16:54:05.814683image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:54:05.840213image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
paypal 47110
33.4%
ach 46844
33.2%
mastercard 15960
 
11.3%
visa 15697
 
11.1%
amex 15639
 
11.1%

Most occurring characters

ValueCountFrequency (%)
a 141837
20.0%
P 94220
13.3%
A 62483
8.8%
y 47110
 
6.7%
l 47110
 
6.7%
C 46844
 
6.6%
H 46844
 
6.6%
r 31920
 
4.5%
s 31657
 
4.5%
e 31599
 
4.5%
Other values (8) 126512
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 708136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 141837
20.0%
P 94220
13.3%
A 62483
8.8%
y 47110
 
6.7%
l 47110
 
6.7%
C 46844
 
6.6%
H 46844
 
6.6%
r 31920
 
4.5%
s 31657
 
4.5%
e 31599
 
4.5%
Other values (8) 126512
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 708136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 141837
20.0%
P 94220
13.3%
A 62483
8.8%
y 47110
 
6.7%
l 47110
 
6.7%
C 46844
 
6.6%
H 46844
 
6.6%
r 31920
 
4.5%
s 31657
 
4.5%
e 31599
 
4.5%
Other values (8) 126512
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 708136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 141837
20.0%
P 94220
13.3%
A 62483
8.8%
y 47110
 
6.7%
l 47110
 
6.7%
C 46844
 
6.6%
H 46844
 
6.6%
r 31920
 
4.5%
s 31657
 
4.5%
e 31599
 
4.5%
Other values (8) 126512
17.9%
Distinct124907
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2025-07-06T16:54:05.970153image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters1836250
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique112330 ?
Unique (%)79.5%

Sample

1st rowPROC-21233551
2nd rowPROC-45142454
3rd rowPROC-76309118
4th rowPROC-62839998
5th rowPROC-62414033
ValueCountFrequency (%)
proc-54910620 4
 
< 0.1%
proc-99885580 4
 
< 0.1%
proc-82847137 4
 
< 0.1%
proc-34872954 4
 
< 0.1%
proc-52280805 4
 
< 0.1%
proc-33425997 4
 
< 0.1%
proc-81472295 4
 
< 0.1%
proc-28936129 4
 
< 0.1%
proc-30105287 4
 
< 0.1%
proc-19700926 4
 
< 0.1%
Other values (124897) 141210
> 99.9%
2025-07-06T16:54:06.134431image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 141250
 
7.7%
R 141250
 
7.7%
O 141250
 
7.7%
C 141250
 
7.7%
- 141250
 
7.7%
7 115128
 
6.3%
8 115060
 
6.3%
1 114741
 
6.2%
4 114512
 
6.2%
3 114444
 
6.2%
Other values (5) 556115
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1836250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 141250
 
7.7%
R 141250
 
7.7%
O 141250
 
7.7%
C 141250
 
7.7%
- 141250
 
7.7%
7 115128
 
6.3%
8 115060
 
6.3%
1 114741
 
6.2%
4 114512
 
6.2%
3 114444
 
6.2%
Other values (5) 556115
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1836250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 141250
 
7.7%
R 141250
 
7.7%
O 141250
 
7.7%
C 141250
 
7.7%
- 141250
 
7.7%
7 115128
 
6.3%
8 115060
 
6.3%
1 114741
 
6.2%
4 114512
 
6.2%
3 114444
 
6.2%
Other values (5) 556115
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1836250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 141250
 
7.7%
R 141250
 
7.7%
O 141250
 
7.7%
C 141250
 
7.7%
- 141250
 
7.7%
7 115128
 
6.3%
8 115060
 
6.3%
1 114741
 
6.2%
4 114512
 
6.2%
3 114444
 
6.2%
Other values (5) 556115
30.3%

BS_Payment_Gateway
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 MiB
Cybersource
47296 
PayPal
47110 
GoCardless
46844 

Length

Max length11
Median length10
Mean length9.0007504
Min length6

Characters and Unicode

Total characters1271356
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPayPal
2nd rowGoCardless
3rd rowGoCardless
4th rowCybersource
5th rowPayPal

Common Values

ValueCountFrequency (%)
Cybersource 47296
33.5%
PayPal 47110
33.4%
GoCardless 46844
33.2%

Length

2025-07-06T16:54:06.168921image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:54:06.190189image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
cybersource 47296
33.5%
paypal 47110
33.4%
gocardless 46844
33.2%

Most occurring characters

ValueCountFrequency (%)
e 141436
11.1%
r 141436
11.1%
a 141064
11.1%
s 140984
11.1%
y 94406
7.4%
P 94220
7.4%
C 94140
7.4%
o 94140
7.4%
l 93954
7.4%
b 47296
 
3.7%
Other values (4) 188280
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1271356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 141436
11.1%
r 141436
11.1%
a 141064
11.1%
s 140984
11.1%
y 94406
7.4%
P 94220
7.4%
C 94140
7.4%
o 94140
7.4%
l 93954
7.4%
b 47296
 
3.7%
Other values (4) 188280
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1271356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 141436
11.1%
r 141436
11.1%
a 141064
11.1%
s 140984
11.1%
y 94406
7.4%
P 94220
7.4%
C 94140
7.4%
o 94140
7.4%
l 93954
7.4%
b 47296
 
3.7%
Other values (4) 188280
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1271356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 141436
11.1%
r 141436
11.1%
a 141064
11.1%
s 140984
11.1%
y 94406
7.4%
P 94220
7.4%
C 94140
7.4%
o 94140
7.4%
l 93954
7.4%
b 47296
 
3.7%
Other values (4) 188280
14.8%

BS_Processor_Response_Code
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size5.5 MiB

BS_Processor_Response_Message
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 MiB
Approved
125000 
Refund Processed
 
12500
Chargeback Initiated
 
2500
Chargeback Reversed
 
1250

Length

Max length20
Median length8
Mean length9.0176991
Min length8

Characters and Unicode

Total characters1273750
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApproved
2nd rowApproved
3rd rowApproved
4th rowApproved
5th rowApproved

Common Values

ValueCountFrequency (%)
Approved 125000
88.5%
Refund Processed 12500
 
8.8%
Chargeback Initiated 2500
 
1.8%
Chargeback Reversed 1250
 
0.9%

Length

2025-07-06T16:54:06.222368image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:54:06.262743image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
approved 125000
79.4%
refund 12500
 
7.9%
processed 12500
 
7.9%
chargeback 3750
 
2.4%
initiated 2500
 
1.6%
reversed 1250
 
0.8%

Most occurring characters

ValueCountFrequency (%)
p 250000
19.6%
e 172500
13.5%
d 153750
12.1%
r 142500
11.2%
o 137500
10.8%
v 126250
9.9%
A 125000
9.8%
s 26250
 
2.1%
c 16250
 
1.3%
16250
 
1.3%
Other values (14) 107500
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1273750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 250000
19.6%
e 172500
13.5%
d 153750
12.1%
r 142500
11.2%
o 137500
10.8%
v 126250
9.9%
A 125000
9.8%
s 26250
 
2.1%
c 16250
 
1.3%
16250
 
1.3%
Other values (14) 107500
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1273750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 250000
19.6%
e 172500
13.5%
d 153750
12.1%
r 142500
11.2%
o 137500
10.8%
v 126250
9.9%
A 125000
9.8%
s 26250
 
2.1%
c 16250
 
1.3%
16250
 
1.3%
Other values (14) 107500
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1273750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 250000
19.6%
e 172500
13.5%
d 153750
12.1%
r 142500
11.2%
o 137500
10.8%
v 126250
9.9%
A 125000
9.8%
s 26250
 
2.1%
c 16250
 
1.3%
16250
 
1.3%
Other values (14) 107500
8.4%
Distinct125000
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Memory size8.1 MiB
2025-07-06T16:54:06.415062image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1553750
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique112500 ?
Unique (%)79.6%

Sample

1st rowPAY-0000000
2nd rowPAY-0000001
3rd rowPAY-0000002
4th rowPAY-0000003
5th rowPAY-0000004
ValueCountFrequency (%)
pay-0038489 4
 
< 0.1%
pay-0033214 4
 
< 0.1%
pay-0066396 4
 
< 0.1%
pay-0063049 4
 
< 0.1%
pay-0124481 4
 
< 0.1%
pay-0064521 4
 
< 0.1%
pay-0113868 4
 
< 0.1%
pay-0030656 4
 
< 0.1%
pay-0032004 4
 
< 0.1%
pay-0063738 4
 
< 0.1%
Other values (124990) 141210
> 99.9%
2025-07-06T16:54:06.623801image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 334091
21.5%
P 141250
9.1%
A 141250
9.1%
Y 141250
9.1%
- 141250
9.1%
1 107672
 
6.9%
2 73838
 
4.8%
4 68511
 
4.4%
3 68239
 
4.4%
5 67331
 
4.3%
Other values (4) 269068
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1553750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 334091
21.5%
P 141250
9.1%
A 141250
9.1%
Y 141250
9.1%
- 141250
9.1%
1 107672
 
6.9%
2 73838
 
4.8%
4 68511
 
4.4%
3 68239
 
4.4%
5 67331
 
4.3%
Other values (4) 269068
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1553750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 334091
21.5%
P 141250
9.1%
A 141250
9.1%
Y 141250
9.1%
- 141250
9.1%
1 107672
 
6.9%
2 73838
 
4.8%
4 68511
 
4.4%
3 68239
 
4.4%
5 67331
 
4.3%
Other values (4) 269068
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1553750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 334091
21.5%
P 141250
9.1%
A 141250
9.1%
Y 141250
9.1%
- 141250
9.1%
1 107672
 
6.9%
2 73838
 
4.8%
4 68511
 
4.4%
3 68239
 
4.4%
5 67331
 
4.3%
Other values (4) 269068
17.3%

BS_Sales_Channel
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
In-Store
47295 
Online
47147 
Phone
46808 

Length

Max length8
Median length6
Mean length6.3382796
Min length5

Characters and Unicode

Total characters895282
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIn-Store
2nd rowIn-Store
3rd rowPhone
4th rowPhone
5th rowIn-Store

Common Values

ValueCountFrequency (%)
In-Store 47295
33.5%
Online 47147
33.4%
Phone 46808
33.1%

Length

2025-07-06T16:54:06.663012image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:54:06.684179image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
in-store 47295
33.5%
online 47147
33.4%
phone 46808
33.1%

Most occurring characters

ValueCountFrequency (%)
n 188397
21.0%
e 141250
15.8%
o 94103
10.5%
I 47295
 
5.3%
- 47295
 
5.3%
S 47295
 
5.3%
t 47295
 
5.3%
r 47295
 
5.3%
O 47147
 
5.3%
l 47147
 
5.3%
Other values (3) 140763
15.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 895282
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 188397
21.0%
e 141250
15.8%
o 94103
10.5%
I 47295
 
5.3%
- 47295
 
5.3%
S 47295
 
5.3%
t 47295
 
5.3%
r 47295
 
5.3%
O 47147
 
5.3%
l 47147
 
5.3%
Other values (3) 140763
15.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 895282
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 188397
21.0%
e 141250
15.8%
o 94103
10.5%
I 47295
 
5.3%
- 47295
 
5.3%
S 47295
 
5.3%
t 47295
 
5.3%
r 47295
 
5.3%
O 47147
 
5.3%
l 47147
 
5.3%
Other values (3) 140763
15.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 895282
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 188397
21.0%
e 141250
15.8%
o 94103
10.5%
I 47295
 
5.3%
- 47295
 
5.3%
S 47295
 
5.3%
t 47295
 
5.3%
r 47295
 
5.3%
O 47147
 
5.3%
l 47147
 
5.3%
Other values (3) 140763
15.7%

BS_Foreign_Exchange_Rate
Real number (ℝ)

High correlation 

Distinct953
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99235357
Minimum0.6534
Maximum1.2827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-06T16:54:06.719383image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0.6534
5-th percentile0.6614
Q10.9933
median1.0016
Q31.01
95-th percentile1.2702
Maximum1.2827
Range0.6293
Interquartile range (IQR)0.0167

Descriptive statistics

Standard deviation0.15062953
Coefficient of variation (CV)0.15179019
Kurtosis0.74384548
Mean0.99235357
Median Absolute Deviation (MAD)0.0083
Skewness-0.5844689
Sum140169.94
Variance0.022689257
MonotonicityNot monotonic
2025-07-06T16:54:06.769350image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0012 481
 
0.3%
0.9918 478
 
0.3%
0.9925 476
 
0.3%
0.997 472
 
0.3%
0.9978 468
 
0.3%
0.9963 465
 
0.3%
0.9905 465
 
0.3%
0.9928 463
 
0.3%
1.0054 462
 
0.3%
1.0005 462
 
0.3%
Other values (943) 136558
96.7%
ValueCountFrequency (%)
0.6534 49
< 0.1%
0.6535 91
0.1%
0.6536 86
0.1%
0.6537 87
0.1%
0.6538 85
0.1%
0.6539 105
0.1%
0.654 83
0.1%
0.6541 105
0.1%
0.6542 96
0.1%
0.6543 73
0.1%
ValueCountFrequency (%)
1.2827 34
< 0.1%
1.2826 58
< 0.1%
1.2825 66
< 0.1%
1.2824 44
< 0.1%
1.2823 46
< 0.1%
1.2822 33
< 0.1%
1.2821 42
< 0.1%
1.282 52
< 0.1%
1.2819 61
< 0.1%
1.2818 56
< 0.1%

GW_Transaction_ID
Text

Missing 

Distinct124128
Distinct (%)98.3%
Missing15000
Missing (%)10.6%
Memory size8.0 MiB
2025-07-06T16:54:06.944312image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1767500
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122024 ?
Unique (%)96.7%

Sample

1st rowGW-TRX-9972261
2nd rowGW-TRX-8455695
3rd rowGW-TRX-5184318
4th rowGW-TRX-8104962
5th rowGW-TRX-2770667
ValueCountFrequency (%)
gw-trx-8805233 4
 
< 0.1%
gw-trx-4653021 3
 
< 0.1%
gw-trx-1392363 3
 
< 0.1%
gw-trx-4044666 3
 
< 0.1%
gw-trx-4979170 3
 
< 0.1%
gw-trx-5701527 3
 
< 0.1%
gw-trx-4420744 3
 
< 0.1%
gw-trx-3932125 3
 
< 0.1%
gw-trx-5469733 3
 
< 0.1%
gw-trx-3951989 3
 
< 0.1%
Other values (124118) 126219
> 99.9%
2025-07-06T16:54:07.145254image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 252500
14.3%
G 126250
 
7.1%
W 126250
 
7.1%
T 126250
 
7.1%
R 126250
 
7.1%
X 126250
 
7.1%
2 90204
 
5.1%
3 89943
 
5.1%
4 89913
 
5.1%
6 89744
 
5.1%
Other values (6) 523946
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1767500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 252500
14.3%
G 126250
 
7.1%
W 126250
 
7.1%
T 126250
 
7.1%
R 126250
 
7.1%
X 126250
 
7.1%
2 90204
 
5.1%
3 89943
 
5.1%
4 89913
 
5.1%
6 89744
 
5.1%
Other values (6) 523946
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1767500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 252500
14.3%
G 126250
 
7.1%
W 126250
 
7.1%
T 126250
 
7.1%
R 126250
 
7.1%
X 126250
 
7.1%
2 90204
 
5.1%
3 89943
 
5.1%
4 89913
 
5.1%
6 89744
 
5.1%
Other values (6) 523946
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1767500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 252500
14.3%
G 126250
 
7.1%
W 126250
 
7.1%
T 126250
 
7.1%
R 126250
 
7.1%
X 126250
 
7.1%
2 90204
 
5.1%
3 89943
 
5.1%
4 89913
 
5.1%
6 89744
 
5.1%
Other values (6) 523946
29.6%

GW_name
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing15000
Missing (%)10.6%
Memory size7.8 MiB
Cybersource
42213 
PayPal
42124 
GoCardless
41913 

Length

Max length11
Median length10
Mean length8.9997386
Min length6

Characters and Unicode

Total characters1136217
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPayPal
2nd rowGoCardless
3rd rowGoCardless
4th rowCybersource
5th rowPayPal

Common Values

ValueCountFrequency (%)
Cybersource 42213
29.9%
PayPal 42124
29.8%
GoCardless 41913
29.7%
(Missing) 15000
 
10.6%

Length

2025-07-06T16:54:07.180966image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:54:07.205078image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
cybersource 42213
33.4%
paypal 42124
33.4%
gocardless 41913
33.2%

Most occurring characters

ValueCountFrequency (%)
e 126339
11.1%
r 126339
11.1%
a 126161
11.1%
s 126039
11.1%
y 84337
7.4%
P 84248
7.4%
C 84126
7.4%
o 84126
7.4%
l 84037
7.4%
b 42213
 
3.7%
Other values (4) 168252
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1136217
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 126339
11.1%
r 126339
11.1%
a 126161
11.1%
s 126039
11.1%
y 84337
7.4%
P 84248
7.4%
C 84126
7.4%
o 84126
7.4%
l 84037
7.4%
b 42213
 
3.7%
Other values (4) 168252
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1136217
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 126339
11.1%
r 126339
11.1%
a 126161
11.1%
s 126039
11.1%
y 84337
7.4%
P 84248
7.4%
C 84126
7.4%
o 84126
7.4%
l 84037
7.4%
b 42213
 
3.7%
Other values (4) 168252
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1136217
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 126339
11.1%
r 126339
11.1%
a 126161
11.1%
s 126039
11.1%
y 84337
7.4%
P 84248
7.4%
C 84126
7.4%
o 84126
7.4%
l 84037
7.4%
b 42213
 
3.7%
Other values (4) 168252
14.8%

GW_Transaction_Status
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing15000
Missing (%)10.6%
Memory size7.5 MiB
settled
126250 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters883750
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsettled
2nd rowsettled
3rd rowsettled
4th rowsettled
5th rowsettled

Common Values

ValueCountFrequency (%)
settled 126250
89.4%
(Missing) 15000
 
10.6%

Length

2025-07-06T16:54:07.236905image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:54:07.255435image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
settled 126250
100.0%

Most occurring characters

ValueCountFrequency (%)
e 252500
28.6%
t 252500
28.6%
s 126250
14.3%
l 126250
14.3%
d 126250
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 883750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 252500
28.6%
t 252500
28.6%
s 126250
14.3%
l 126250
14.3%
d 126250
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 883750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 252500
28.6%
t 252500
28.6%
s 126250
14.3%
l 126250
14.3%
d 126250
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 883750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 252500
28.6%
t 252500
28.6%
s 126250
14.3%
l 126250
14.3%
d 126250
14.3%

GW_Response_Code
Unsupported

Missing  Rejected  Unsupported 

Missing15000
Missing (%)10.6%
Memory size5.2 MiB

GW_Response
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing15000
Missing (%)10.6%
Memory size7.7 MiB
Approved
126250 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1010000
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApproved
2nd rowApproved
3rd rowApproved
4th rowApproved
5th rowApproved

Common Values

ValueCountFrequency (%)
Approved 126250
89.4%
(Missing) 15000
 
10.6%

Length

2025-07-06T16:54:07.279214image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:54:07.298112image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
approved 126250
100.0%

Most occurring characters

ValueCountFrequency (%)
p 252500
25.0%
A 126250
12.5%
r 126250
12.5%
o 126250
12.5%
v 126250
12.5%
e 126250
12.5%
d 126250
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1010000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 252500
25.0%
A 126250
12.5%
r 126250
12.5%
o 126250
12.5%
v 126250
12.5%
e 126250
12.5%
d 126250
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1010000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 252500
25.0%
A 126250
12.5%
r 126250
12.5%
o 126250
12.5%
v 126250
12.5%
e 126250
12.5%
d 126250
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1010000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 252500
25.0%
A 126250
12.5%
r 126250
12.5%
o 126250
12.5%
v 126250
12.5%
e 126250
12.5%
d 126250
12.5%

GW_Transaction_Amount
Real number (ℝ)

High correlation  Missing 

Distinct110454
Distinct (%)87.5%
Missing15000
Missing (%)10.6%
Infinite0
Infinite (%)0.0%
Mean2513.3731
Minimum25.08
Maximum4999.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-06T16:54:07.327735image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum25.08
5-th percentile270.357
Q11269.245
median2512.08
Q33756.1275
95-th percentile4751.42
Maximum4999.87
Range4974.79
Interquartile range (IQR)2486.8825

Descriptive statistics

Standard deviation1436.5054
Coefficient of variation (CV)0.57154485
Kurtosis-1.1975413
Mean2513.3731
Median Absolute Deviation (MAD)1243.5
Skewness0.00018353774
Sum3.1731335 × 108
Variance2063547.9
MonotonicityNot monotonic
2025-07-06T16:54:07.374264image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3543.24 5
 
< 0.1%
4756.12 5
 
< 0.1%
1902 5
 
< 0.1%
4146.9 5
 
< 0.1%
2595.9 5
 
< 0.1%
931.3 5
 
< 0.1%
1020.37 5
 
< 0.1%
953.97 5
 
< 0.1%
4610.19 4
 
< 0.1%
659.95 4
 
< 0.1%
Other values (110444) 126202
89.3%
(Missing) 15000
 
10.6%
ValueCountFrequency (%)
25.08 1
< 0.1%
25.15 1
< 0.1%
25.16 1
< 0.1%
25.31 1
< 0.1%
25.32 1
< 0.1%
25.34 1
< 0.1%
25.37 2
< 0.1%
25.4 1
< 0.1%
25.47 1
< 0.1%
25.48 1
< 0.1%
ValueCountFrequency (%)
4999.87 1
< 0.1%
4999.85 2
< 0.1%
4999.66 1
< 0.1%
4999.61 1
< 0.1%
4999.54 1
< 0.1%
4999.44 1
< 0.1%
4999.28 2
< 0.1%
4999.27 1
< 0.1%
4999.26 1
< 0.1%
4999.24 1
< 0.1%

GW_Trx_Currency
Categorical

High correlation  Missing 

Distinct5
Distinct (%)< 0.1%
Missing15000
Missing (%)10.6%
Memory size7.1 MiB
USD
75838 
EUR
18783 
GBP
12585 
AUD
10152 
CAD
8892 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters378750
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowGBP
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 75838
53.7%
EUR 18783
 
13.3%
GBP 12585
 
8.9%
AUD 10152
 
7.2%
CAD 8892
 
6.3%
(Missing) 15000
 
10.6%

Length

2025-07-06T16:54:07.416916image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:54:07.442740image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
usd 75838
60.1%
eur 18783
 
14.9%
gbp 12585
 
10.0%
aud 10152
 
8.0%
cad 8892
 
7.0%

Most occurring characters

ValueCountFrequency (%)
U 104773
27.7%
D 94882
25.1%
S 75838
20.0%
A 19044
 
5.0%
E 18783
 
5.0%
R 18783
 
5.0%
G 12585
 
3.3%
B 12585
 
3.3%
P 12585
 
3.3%
C 8892
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 378750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 104773
27.7%
D 94882
25.1%
S 75838
20.0%
A 19044
 
5.0%
E 18783
 
5.0%
R 18783
 
5.0%
G 12585
 
3.3%
B 12585
 
3.3%
P 12585
 
3.3%
C 8892
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 378750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 104773
27.7%
D 94882
25.1%
S 75838
20.0%
A 19044
 
5.0%
E 18783
 
5.0%
R 18783
 
5.0%
G 12585
 
3.3%
B 12585
 
3.3%
P 12585
 
3.3%
C 8892
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 378750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 104773
27.7%
D 94882
25.1%
S 75838
20.0%
A 19044
 
5.0%
E 18783
 
5.0%
R 18783
 
5.0%
G 12585
 
3.3%
B 12585
 
3.3%
P 12585
 
3.3%
C 8892
 
2.3%

GW_Trx_Type
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing15000
Missing (%)10.6%
Memory size7.5 MiB
payment
126250 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters883750
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpayment
2nd rowpayment
3rd rowpayment
4th rowpayment
5th rowpayment

Common Values

ValueCountFrequency (%)
payment 126250
89.4%
(Missing) 15000
 
10.6%

Length

2025-07-06T16:54:07.479259image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:54:07.500882image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
payment 126250
100.0%

Most occurring characters

ValueCountFrequency (%)
p 126250
14.3%
a 126250
14.3%
y 126250
14.3%
m 126250
14.3%
e 126250
14.3%
n 126250
14.3%
t 126250
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 883750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 126250
14.3%
a 126250
14.3%
y 126250
14.3%
m 126250
14.3%
e 126250
14.3%
n 126250
14.3%
t 126250
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 883750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 126250
14.3%
a 126250
14.3%
y 126250
14.3%
m 126250
14.3%
e 126250
14.3%
n 126250
14.3%
t 126250
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 883750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 126250
14.3%
a 126250
14.3%
y 126250
14.3%
m 126250
14.3%
e 126250
14.3%
n 126250
14.3%
t 126250
14.3%

GW_Card_Brand
Categorical

High correlation  Missing 

Distinct5
Distinct (%)< 0.1%
Missing15000
Missing (%)10.6%
Memory size7.6 MiB
PayPal
42124 
GoCardless
41913 
Mastercard
14223 
Visa
13996 
Amex
13994 

Length

Max length10
Median length6
Mean length7.3351604
Min length4

Characters and Unicode

Total characters926064
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPayPal
2nd rowGoCardless
3rd rowGoCardless
4th rowVisa
5th rowPayPal

Common Values

ValueCountFrequency (%)
PayPal 42124
29.8%
GoCardless 41913
29.7%
Mastercard 14223
 
10.1%
Visa 13996
 
9.9%
Amex 13994
 
9.9%
(Missing) 15000
 
10.6%

Length

2025-07-06T16:54:07.526515image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:54:07.553392image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
paypal 42124
33.4%
gocardless 41913
33.2%
mastercard 14223
 
11.3%
visa 13996
 
11.1%
amex 13994
 
11.1%

Most occurring characters

ValueCountFrequency (%)
a 168603
18.2%
s 112045
12.1%
P 84248
9.1%
l 84037
9.1%
r 70359
7.6%
e 70130
7.6%
d 56136
 
6.1%
y 42124
 
4.5%
C 41913
 
4.5%
o 41913
 
4.5%
Other values (9) 154556
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 926064
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 168603
18.2%
s 112045
12.1%
P 84248
9.1%
l 84037
9.1%
r 70359
7.6%
e 70130
7.6%
d 56136
 
6.1%
y 42124
 
4.5%
C 41913
 
4.5%
o 41913
 
4.5%
Other values (9) 154556
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 926064
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 168603
18.2%
s 112045
12.1%
P 84248
9.1%
l 84037
9.1%
r 70359
7.6%
e 70130
7.6%
d 56136
 
6.1%
y 42124
 
4.5%
C 41913
 
4.5%
o 41913
 
4.5%
Other values (9) 154556
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 926064
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 168603
18.2%
s 112045
12.1%
P 84248
9.1%
l 84037
9.1%
r 70359
7.6%
e 70130
7.6%
d 56136
 
6.1%
y 42124
 
4.5%
C 41913
 
4.5%
o 41913
 
4.5%
Other values (9) 154556
16.7%

GW_fees
Real number (ℝ)

High correlation  Missing 

Distinct4995
Distinct (%)4.0%
Missing15000
Missing (%)10.6%
Infinite0
Infinite (%)0.0%
Mean16.786014
Minimum0.06
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-06T16:54:07.596550image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile1.3
Q16.03
median11.95
Q326.93
95-th percentile42.59
Maximum50
Range49.94
Interquartile range (IQR)20.9

Descriptive statistics

Standard deviation13.169925
Coefficient of variation (CV)0.78457725
Kurtosis-0.59754096
Mean16.786014
Median Absolute Deviation (MAD)8.39
Skewness0.72097683
Sum2119234.3
Variance173.44692
MonotonicityNot monotonic
2025-07-06T16:54:07.643667image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.94 77
 
0.1%
0.73 76
 
0.1%
3.19 75
 
0.1%
10.64 75
 
0.1%
5.52 75
 
0.1%
7.95 74
 
0.1%
5.82 74
 
0.1%
7.99 73
 
0.1%
4.84 72
 
0.1%
2.21 72
 
0.1%
Other values (4985) 125507
88.9%
(Missing) 15000
 
10.6%
ValueCountFrequency (%)
0.06 12
 
< 0.1%
0.07 34
< 0.1%
0.08 41
< 0.1%
0.09 26
< 0.1%
0.1 37
< 0.1%
0.11 38
< 0.1%
0.12 39
< 0.1%
0.13 33
< 0.1%
0.14 29
< 0.1%
0.15 40
< 0.1%
ValueCountFrequency (%)
50 2
 
< 0.1%
49.99 6
< 0.1%
49.98 6
< 0.1%
49.97 4
 
< 0.1%
49.96 12
< 0.1%
49.95 11
< 0.1%
49.94 8
< 0.1%
49.93 12
< 0.1%
49.92 6
< 0.1%
49.91 6
< 0.1%

GW_Net_Settled_Amt
Real number (ℝ)

High correlation  Missing 

Distinct112419
Distinct (%)89.0%
Missing15000
Missing (%)10.6%
Infinite0
Infinite (%)0.0%
Mean2496.5871
Minimum24.89
Maximum4987.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-06T16:54:07.689569image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum24.89
5-th percentile268.358
Q11260.2175
median2495.195
Q33731.845
95-th percentile4721.22
Maximum4987.37
Range4962.48
Interquartile range (IQR)2471.6275

Descriptive statistics

Standard deviation1426.9339
Coefficient of variation (CV)0.57155383
Kurtosis-1.1973898
Mean2496.5871
Median Absolute Deviation (MAD)1235.585
Skewness0.00028834919
Sum3.1519412 × 108
Variance2036140.4
MonotonicityNot monotonic
2025-07-06T16:54:07.736523image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2897.74 5
 
< 0.1%
3385.57 5
 
< 0.1%
3300.21 5
 
< 0.1%
4796.47 5
 
< 0.1%
3598.04 4
 
< 0.1%
4285.42 4
 
< 0.1%
1161.27 4
 
< 0.1%
1456.09 4
 
< 0.1%
2408.8 4
 
< 0.1%
3891.57 4
 
< 0.1%
Other values (112409) 126206
89.3%
(Missing) 15000
 
10.6%
ValueCountFrequency (%)
24.89 1
< 0.1%
24.96 1
< 0.1%
25.1 1
< 0.1%
25.12 1
< 0.1%
25.22 1
< 0.1%
25.23 1
< 0.1%
25.26 1
< 0.1%
25.28 1
< 0.1%
25.31 2
< 0.1%
25.34 1
< 0.1%
ValueCountFrequency (%)
4987.37 1
< 0.1%
4987.35 1
< 0.1%
4987.11 1
< 0.1%
4987.04 1
< 0.1%
4986.94 1
< 0.1%
4986.78 2
< 0.1%
4986.68 1
< 0.1%
4986.35 1
< 0.1%
4986.34 1
< 0.1%
4986.23 1
< 0.1%

GW_Trx_Date
Date

Missing 

Distinct90
Distinct (%)0.1%
Missing15000
Missing (%)10.6%
Memory size1.1 MiB
Minimum2025-01-01 00:00:00
Maximum2025-03-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-06T16:54:07.782108image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:07.833120image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

GW_Ref_Number
Text

Missing 

Distinct125000
Distinct (%)99.0%
Missing15000
Missing (%)10.6%
Memory size7.7 MiB
2025-07-06T16:54:08.025334image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1388750
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique123750 ?
Unique (%)98.0%

Sample

1st rowPAY-0000000
2nd rowPAY-0000001
3rd rowPAY-0000002
4th rowPAY-0000003
5th rowPAY-0000004
ValueCountFrequency (%)
pay-0118884 2
 
< 0.1%
pay-0110496 2
 
< 0.1%
pay-0015196 2
 
< 0.1%
pay-0046112 2
 
< 0.1%
pay-0046204 2
 
< 0.1%
pay-0110180 2
 
< 0.1%
pay-0077288 2
 
< 0.1%
pay-0077050 2
 
< 0.1%
pay-0079924 2
 
< 0.1%
pay-0048439 2
 
< 0.1%
Other values (124990) 126230
> 99.9%
2025-07-06T16:54:08.239764image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 298496
21.5%
P 126250
9.1%
A 126250
9.1%
Y 126250
9.1%
- 126250
9.1%
1 96397
 
6.9%
2 66100
 
4.8%
4 61140
 
4.4%
3 61114
 
4.4%
8 60139
 
4.3%
Other values (4) 240364
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1388750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 298496
21.5%
P 126250
9.1%
A 126250
9.1%
Y 126250
9.1%
- 126250
9.1%
1 96397
 
6.9%
2 66100
 
4.8%
4 61140
 
4.4%
3 61114
 
4.4%
8 60139
 
4.3%
Other values (4) 240364
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1388750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 298496
21.5%
P 126250
9.1%
A 126250
9.1%
Y 126250
9.1%
- 126250
9.1%
1 96397
 
6.9%
2 66100
 
4.8%
4 61140
 
4.4%
3 61114
 
4.4%
8 60139
 
4.3%
Other values (4) 240364
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1388750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 298496
21.5%
P 126250
9.1%
A 126250
9.1%
Y 126250
9.1%
- 126250
9.1%
1 96397
 
6.9%
2 66100
 
4.8%
4 61140
 
4.4%
3 61114
 
4.4%
8 60139
 
4.3%
Other values (4) 240364
17.3%

BS_GW_Match
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size138.1 KiB
True
126250 
False
15000 
ValueCountFrequency (%)
True 126250
89.4%
False 15000
 
10.6%
2025-07-06T16:54:08.360194image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

MP_Trx_ID
Text

Missing 

Distinct124946
Distinct (%)> 99.9%
Missing16250
Missing (%)11.5%
Memory size8.4 MiB
2025-07-06T16:54:08.504425image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters2125000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124892 ?
Unique (%)99.9%

Sample

1st rowPROC-TRX-96148747
2nd rowPROC-TRX-53756634
3rd rowPROC-TRX-96643401
4th rowPROC-TRX-48503361
5th rowPROC-TRX-87613870
ValueCountFrequency (%)
proc-trx-20751798 2
 
< 0.1%
proc-trx-47008295 2
 
< 0.1%
proc-trx-18122493 2
 
< 0.1%
proc-trx-77205797 2
 
< 0.1%
proc-trx-72429823 2
 
< 0.1%
proc-trx-59325275 2
 
< 0.1%
proc-trx-28064569 2
 
< 0.1%
proc-trx-77635747 2
 
< 0.1%
proc-trx-93071076 2
 
< 0.1%
proc-trx-76043245 2
 
< 0.1%
Other values (124936) 124980
> 99.9%
2025-07-06T16:54:08.692460image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 250000
 
11.8%
R 250000
 
11.8%
P 125000
 
5.9%
O 125000
 
5.9%
C 125000
 
5.9%
T 125000
 
5.9%
X 125000
 
5.9%
4 101764
 
4.8%
8 101637
 
4.8%
2 101575
 
4.8%
Other values (7) 695024
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2125000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 250000
 
11.8%
R 250000
 
11.8%
P 125000
 
5.9%
O 125000
 
5.9%
C 125000
 
5.9%
T 125000
 
5.9%
X 125000
 
5.9%
4 101764
 
4.8%
8 101637
 
4.8%
2 101575
 
4.8%
Other values (7) 695024
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2125000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 250000
 
11.8%
R 250000
 
11.8%
P 125000
 
5.9%
O 125000
 
5.9%
C 125000
 
5.9%
T 125000
 
5.9%
X 125000
 
5.9%
4 101764
 
4.8%
8 101637
 
4.8%
2 101575
 
4.8%
Other values (7) 695024
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2125000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 250000
 
11.8%
R 250000
 
11.8%
P 125000
 
5.9%
O 125000
 
5.9%
C 125000
 
5.9%
T 125000
 
5.9%
X 125000
 
5.9%
4 101764
 
4.8%
8 101637
 
4.8%
2 101575
 
4.8%
Other values (7) 695024
32.7%

MP_Processor_Type
Categorical

High correlation  Missing 

Distinct4
Distinct (%)< 0.1%
Missing16250
Missing (%)11.5%
Memory size7.7 MiB
PayPal
41715 
GoCardless
41514 
ClientLine
27924 
Amex
13847 

Length

Max length10
Median length10
Mean length8.000464
Min length4

Characters and Unicode

Total characters1000058
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPayPal
2nd rowGoCardless
3rd rowGoCardless
4th rowClientLine
5th rowPayPal

Common Values

ValueCountFrequency (%)
PayPal 41715
29.5%
GoCardless 41514
29.4%
ClientLine 27924
19.8%
Amex 13847
 
9.8%
(Missing) 16250
 
11.5%

Length

2025-07-06T16:54:08.730284image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:54:08.755261image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
paypal 41715
33.4%
gocardless 41514
33.2%
clientline 27924
22.3%
amex 13847
 
11.1%

Most occurring characters

ValueCountFrequency (%)
a 124944
12.5%
e 111209
11.1%
l 111153
11.1%
P 83430
 
8.3%
s 83028
 
8.3%
C 69438
 
6.9%
i 55848
 
5.6%
n 55848
 
5.6%
y 41715
 
4.2%
G 41514
 
4.2%
Other values (8) 221931
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000058
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 124944
12.5%
e 111209
11.1%
l 111153
11.1%
P 83430
 
8.3%
s 83028
 
8.3%
C 69438
 
6.9%
i 55848
 
5.6%
n 55848
 
5.6%
y 41715
 
4.2%
G 41514
 
4.2%
Other values (8) 221931
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000058
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 124944
12.5%
e 111209
11.1%
l 111153
11.1%
P 83430
 
8.3%
s 83028
 
8.3%
C 69438
 
6.9%
i 55848
 
5.6%
n 55848
 
5.6%
y 41715
 
4.2%
G 41514
 
4.2%
Other values (8) 221931
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000058
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 124944
12.5%
e 111209
11.1%
l 111153
11.1%
P 83430
 
8.3%
s 83028
 
8.3%
C 69438
 
6.9%
i 55848
 
5.6%
n 55848
 
5.6%
y 41715
 
4.2%
G 41514
 
4.2%
Other values (8) 221931
22.2%

MP_Trx_Type
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing16250
Missing (%)11.5%
Memory size7.5 MiB
payment
125000 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters875000
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpayment
2nd rowpayment
3rd rowpayment
4th rowpayment
5th rowpayment

Common Values

ValueCountFrequency (%)
payment 125000
88.5%
(Missing) 16250
 
11.5%

Length

2025-07-06T16:54:08.790777image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:54:08.810812image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
payment 125000
100.0%

Most occurring characters

ValueCountFrequency (%)
p 125000
14.3%
a 125000
14.3%
y 125000
14.3%
m 125000
14.3%
e 125000
14.3%
n 125000
14.3%
t 125000
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 875000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 125000
14.3%
a 125000
14.3%
y 125000
14.3%
m 125000
14.3%
e 125000
14.3%
n 125000
14.3%
t 125000
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 875000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 125000
14.3%
a 125000
14.3%
y 125000
14.3%
m 125000
14.3%
e 125000
14.3%
n 125000
14.3%
t 125000
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 875000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 125000
14.3%
a 125000
14.3%
y 125000
14.3%
m 125000
14.3%
e 125000
14.3%
n 125000
14.3%
t 125000
14.3%

MP_Processor_Fee
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct7087
Distinct (%)5.7%
Missing16250
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean9.7874818
Minimum0
Maximum74.99
Zeros83229
Zeros (%)58.9%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-06T16:54:08.843121image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314.34
95-th percentile47.96
Maximum74.99
Range74.99
Interquartile range (IQR)14.34

Descriptive statistics

Standard deviation17.322844
Coefficient of variation (CV)1.769898
Kurtosis2.0055467
Mean9.7874818
Median Absolute Deviation (MAD)0
Skewness1.7338711
Sum1223435.2
Variance300.08092
MonotonicityNot monotonic
2025-07-06T16:54:08.886646image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 83229
58.9%
28.66 18
 
< 0.1%
17.38 18
 
< 0.1%
20.4 18
 
< 0.1%
12.85 18
 
< 0.1%
49.61 18
 
< 0.1%
22.71 17
 
< 0.1%
25.57 17
 
< 0.1%
15.22 17
 
< 0.1%
2.85 17
 
< 0.1%
Other values (7077) 41613
29.5%
(Missing) 16250
 
11.5%
ValueCountFrequency (%)
0 83229
58.9%
0.26 5
 
< 0.1%
0.27 9
 
< 0.1%
0.28 7
 
< 0.1%
0.29 3
 
< 0.1%
0.3 9
 
< 0.1%
0.31 2
 
< 0.1%
0.32 4
 
< 0.1%
0.33 6
 
< 0.1%
0.34 3
 
< 0.1%
ValueCountFrequency (%)
74.99 1
 
< 0.1%
74.98 3
< 0.1%
74.97 2
< 0.1%
74.95 2
< 0.1%
74.94 4
< 0.1%
74.93 3
< 0.1%
74.92 1
 
< 0.1%
74.91 1
 
< 0.1%
74.9 2
< 0.1%
74.89 2
< 0.1%

MP_Settled_Amount
Real number (ℝ)

High correlation  Missing 

Distinct113685
Distinct (%)90.9%
Missing16250
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean2486.9651
Minimum24.84
Maximum4987.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-06T16:54:08.934637image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum24.84
5-th percentile267.5695
Q11255.88
median2486.03
Q33715.785
95-th percentile4704.0705
Maximum4987.37
Range4962.53
Interquartile range (IQR)2459.905

Descriptive statistics

Standard deviation1421.6569
Coefficient of variation (CV)0.57164327
Kurtosis-1.1966635
Mean2486.9651
Median Absolute Deviation (MAD)1230.085
Skewness0.0010518471
Sum3.1087064 × 108
Variance2021108.2
MonotonicityNot monotonic
2025-07-06T16:54:08.979363image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3598.04 5
 
< 0.1%
398.52 5
 
< 0.1%
513 4
 
< 0.1%
3612.01 4
 
< 0.1%
257.12 4
 
< 0.1%
2602.42 4
 
< 0.1%
3613.72 4
 
< 0.1%
1660.32 4
 
< 0.1%
4664.19 4
 
< 0.1%
1439.83 4
 
< 0.1%
Other values (113675) 124958
88.5%
(Missing) 16250
 
11.5%
ValueCountFrequency (%)
24.84 1
< 0.1%
24.85 1
< 0.1%
24.89 1
< 0.1%
24.96 1
< 0.1%
25.1 1
< 0.1%
25.11 1
< 0.1%
25.12 1
< 0.1%
25.26 1
< 0.1%
25.28 1
< 0.1%
25.31 2
< 0.1%
ValueCountFrequency (%)
4987.37 1
< 0.1%
4987.35 1
< 0.1%
4987.11 1
< 0.1%
4987.04 1
< 0.1%
4986.94 1
< 0.1%
4986.78 2
< 0.1%
4986.68 1
< 0.1%
4986.35 1
< 0.1%
4986.34 1
< 0.1%
4986.23 1
< 0.1%

MP_Settlement_Date
Date

Missing 

Distinct65
Distinct (%)0.1%
Missing16250
Missing (%)11.5%
Memory size1.1 MiB
Minimum2025-01-02 00:00:00
Maximum2025-04-02 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-06T16:54:09.027329image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:09.080722image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

MP_Settlement_Batch
Text

Missing 

Distinct2250
Distinct (%)1.8%
Missing16250
Missing (%)11.5%
Memory size9.9 MiB
2025-07-06T16:54:09.218914image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length38
Median length32
Mean length30.010328
Min length26

Characters and Unicode

Total characters3751291
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSET-20250130-PayPal-N/A-USD
2nd rowSET-20250110-GoCardless-N/A-USD
3rd rowSET-20250221-GoCardless-N/A-USD
4th rowSET-20250104-ClientLine-Visa-GBP
5th rowSET-20250316-PayPal-N/A-USD
ValueCountFrequency (%)
set-20250120-paypal-n/a-usd 318
 
0.3%
set-20250202-paypal-n/a-usd 316
 
0.3%
set-20250316-paypal-n/a-usd 311
 
0.2%
set-20250210-gocardless-n/a-usd 311
 
0.2%
set-20250313-gocardless-n/a-usd 311
 
0.2%
set-20250102-paypal-n/a-usd 310
 
0.2%
set-20250112-paypal-n/a-usd 309
 
0.2%
set-20250121-paypal-n/a-usd 309
 
0.2%
set-20250101-paypal-n/a-usd 308
 
0.2%
set-20250214-paypal-n/a-usd 307
 
0.2%
Other values (2240) 121890
97.5%
2025-07-06T16:54:09.381848image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 500000
 
13.3%
2 341956
 
9.1%
0 298474
 
8.0%
S 200092
 
5.3%
a 166945
 
4.5%
E 143598
 
3.8%
e 139133
 
3.7%
5 137569
 
3.7%
A 129778
 
3.5%
T 125000
 
3.3%
Other values (31) 1568746
41.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3751291
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 500000
 
13.3%
2 341956
 
9.1%
0 298474
 
8.0%
S 200092
 
5.3%
a 166945
 
4.5%
E 143598
 
3.8%
e 139133
 
3.7%
5 137569
 
3.7%
A 129778
 
3.5%
T 125000
 
3.3%
Other values (31) 1568746
41.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3751291
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 500000
 
13.3%
2 341956
 
9.1%
0 298474
 
8.0%
S 200092
 
5.3%
a 166945
 
4.5%
E 143598
 
3.8%
e 139133
 
3.7%
5 137569
 
3.7%
A 129778
 
3.5%
T 125000
 
3.3%
Other values (31) 1568746
41.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3751291
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 500000
 
13.3%
2 341956
 
9.1%
0 298474
 
8.0%
S 200092
 
5.3%
a 166945
 
4.5%
E 143598
 
3.8%
e 139133
 
3.7%
5 137569
 
3.7%
A 129778
 
3.5%
T 125000
 
3.3%
Other values (31) 1568746
41.8%

MP_Trx_Currency
Categorical

High correlation  Missing 

Distinct5
Distinct (%)< 0.1%
Missing16250
Missing (%)11.5%
Memory size7.1 MiB
USD
75092 
EUR
18598 
GBP
12455 
AUD
10059 
CAD
8796 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters375000
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowGBP
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 75092
53.2%
EUR 18598
 
13.2%
GBP 12455
 
8.8%
AUD 10059
 
7.1%
CAD 8796
 
6.2%
(Missing) 16250
 
11.5%

Length

2025-07-06T16:54:09.420838image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:54:09.447627image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
usd 75092
60.1%
eur 18598
 
14.9%
gbp 12455
 
10.0%
aud 10059
 
8.0%
cad 8796
 
7.0%

Most occurring characters

ValueCountFrequency (%)
U 103749
27.7%
D 93947
25.1%
S 75092
20.0%
A 18855
 
5.0%
E 18598
 
5.0%
R 18598
 
5.0%
G 12455
 
3.3%
B 12455
 
3.3%
P 12455
 
3.3%
C 8796
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 375000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 103749
27.7%
D 93947
25.1%
S 75092
20.0%
A 18855
 
5.0%
E 18598
 
5.0%
R 18598
 
5.0%
G 12455
 
3.3%
B 12455
 
3.3%
P 12455
 
3.3%
C 8796
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 375000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 103749
27.7%
D 93947
25.1%
S 75092
20.0%
A 18855
 
5.0%
E 18598
 
5.0%
R 18598
 
5.0%
G 12455
 
3.3%
B 12455
 
3.3%
P 12455
 
3.3%
C 8796
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 375000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 103749
27.7%
D 93947
25.1%
S 75092
20.0%
A 18855
 
5.0%
E 18598
 
5.0%
R 18598
 
5.0%
G 12455
 
3.3%
B 12455
 
3.3%
P 12455
 
3.3%
C 8796
 
2.3%

MP_Order_ID
Text

Missing 

Distinct125000
Distinct (%)100.0%
Missing16250
Missing (%)11.5%
Memory size7.6 MiB
2025-07-06T16:54:09.619802image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1375000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique125000 ?
Unique (%)100.0%

Sample

1st rowPAY-0000000
2nd rowPAY-0000001
3rd rowPAY-0000002
4th rowPAY-0000003
5th rowPAY-0000004
ValueCountFrequency (%)
pay-0083344 1
 
< 0.1%
pay-0000002 1
 
< 0.1%
pay-0000004 1
 
< 0.1%
pay-0000005 1
 
< 0.1%
pay-0000006 1
 
< 0.1%
pay-0000007 1
 
< 0.1%
pay-0000008 1
 
< 0.1%
pay-0000009 1
 
< 0.1%
pay-0000010 1
 
< 0.1%
pay-0000011 1
 
< 0.1%
Other values (124990) 124990
> 99.9%
2025-07-06T16:54:09.830143image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 295500
21.5%
P 125000
9.1%
A 125000
9.1%
Y 125000
9.1%
- 125000
9.1%
1 95500
 
6.9%
2 65500
 
4.8%
3 60500
 
4.4%
4 60500
 
4.4%
8 59500
 
4.3%
Other values (4) 238000
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1375000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 295500
21.5%
P 125000
9.1%
A 125000
9.1%
Y 125000
9.1%
- 125000
9.1%
1 95500
 
6.9%
2 65500
 
4.8%
3 60500
 
4.4%
4 60500
 
4.4%
8 59500
 
4.3%
Other values (4) 238000
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1375000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 295500
21.5%
P 125000
9.1%
A 125000
9.1%
Y 125000
9.1%
- 125000
9.1%
1 95500
 
6.9%
2 65500
 
4.8%
3 60500
 
4.4%
4 60500
 
4.4%
8 59500
 
4.3%
Other values (4) 238000
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1375000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 295500
21.5%
P 125000
9.1%
A 125000
9.1%
Y 125000
9.1%
- 125000
9.1%
1 95500
 
6.9%
2 65500
 
4.8%
3 60500
 
4.4%
4 60500
 
4.4%
8 59500
 
4.3%
Other values (4) 238000
17.3%

PG_MP_Match
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size138.1 KiB
True
125000 
False
16250 
ValueCountFrequency (%)
True 125000
88.5%
False 16250
 
11.5%
2025-07-06T16:54:09.854657image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Bank_Deposit_Date
Date

Missing 

Distinct63
Distinct (%)0.1%
Missing20362
Missing (%)14.4%
Memory size1.1 MiB
Minimum2025-01-03 00:00:00
Maximum2025-04-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-06T16:54:09.885685image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:09.931781image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Bank_Deposit_Currency
Categorical

High correlation  Missing 

Distinct5
Distinct (%)< 0.1%
Missing20362
Missing (%)14.4%
Memory size7.1 MiB
USD
72643 
EUR
17956 
GBP
12059 
AUD
9725 
CAD
8505 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters362664
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowGBP
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 72643
51.4%
EUR 17956
 
12.7%
GBP 12059
 
8.5%
AUD 9725
 
6.9%
CAD 8505
 
6.0%
(Missing) 20362
 
14.4%

Length

2025-07-06T16:54:09.972395image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:54:09.997341image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
usd 72643
60.1%
eur 17956
 
14.9%
gbp 12059
 
10.0%
aud 9725
 
8.0%
cad 8505
 
7.0%

Most occurring characters

ValueCountFrequency (%)
U 100324
27.7%
D 90873
25.1%
S 72643
20.0%
A 18230
 
5.0%
E 17956
 
5.0%
R 17956
 
5.0%
G 12059
 
3.3%
B 12059
 
3.3%
P 12059
 
3.3%
C 8505
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 362664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 100324
27.7%
D 90873
25.1%
S 72643
20.0%
A 18230
 
5.0%
E 17956
 
5.0%
R 17956
 
5.0%
G 12059
 
3.3%
B 12059
 
3.3%
P 12059
 
3.3%
C 8505
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 362664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 100324
27.7%
D 90873
25.1%
S 72643
20.0%
A 18230
 
5.0%
E 17956
 
5.0%
R 17956
 
5.0%
G 12059
 
3.3%
B 12059
 
3.3%
P 12059
 
3.3%
C 8505
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 362664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 100324
27.7%
D 90873
25.1%
S 72643
20.0%
A 18230
 
5.0%
E 17956
 
5.0%
R 17956
 
5.0%
G 12059
 
3.3%
B 12059
 
3.3%
P 12059
 
3.3%
C 8505
 
2.3%
Distinct2175
Distinct (%)1.8%
Missing20362
Missing (%)14.4%
Memory size9.7 MiB
2025-07-06T16:54:10.126646image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length38
Median length32
Mean length30.008669
Min length26

Characters and Unicode

Total characters3627688
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSET-20250130-PayPal-N/A-USD
2nd rowSET-20250110-GoCardless-N/A-USD
3rd rowSET-20250221-GoCardless-N/A-USD
4th rowSET-20250104-ClientLine-Visa-GBP
5th rowSET-20250316-PayPal-N/A-USD
ValueCountFrequency (%)
set-20250120-paypal-n/a-usd 318
 
0.3%
set-20250202-paypal-n/a-usd 316
 
0.3%
set-20250313-gocardless-n/a-usd 311
 
0.3%
set-20250210-gocardless-n/a-usd 311
 
0.3%
set-20250316-paypal-n/a-usd 311
 
0.3%
set-20250102-paypal-n/a-usd 310
 
0.3%
set-20250112-paypal-n/a-usd 309
 
0.3%
set-20250121-paypal-n/a-usd 309
 
0.3%
set-20250101-paypal-n/a-usd 308
 
0.3%
set-20250214-paypal-n/a-usd 307
 
0.3%
Other values (2165) 117778
97.4%
2025-07-06T16:54:10.286475image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 483552
 
13.3%
2 332380
 
9.2%
0 288873
 
8.0%
S 193531
 
5.3%
a 161527
 
4.5%
E 138844
 
3.8%
e 134480
 
3.7%
5 133457
 
3.7%
A 125490
 
3.5%
T 120888
 
3.3%
Other values (31) 1514666
41.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3627688
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 483552
 
13.3%
2 332380
 
9.2%
0 288873
 
8.0%
S 193531
 
5.3%
a 161527
 
4.5%
E 138844
 
3.8%
e 134480
 
3.7%
5 133457
 
3.7%
A 125490
 
3.5%
T 120888
 
3.3%
Other values (31) 1514666
41.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3627688
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 483552
 
13.3%
2 332380
 
9.2%
0 288873
 
8.0%
S 193531
 
5.3%
a 161527
 
4.5%
E 138844
 
3.8%
e 134480
 
3.7%
5 133457
 
3.7%
A 125490
 
3.5%
T 120888
 
3.3%
Other values (31) 1514666
41.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3627688
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 483552
 
13.3%
2 332380
 
9.2%
0 288873
 
8.0%
S 193531
 
5.3%
a 161527
 
4.5%
E 138844
 
3.8%
e 134480
 
3.7%
5 133457
 
3.7%
A 125490
 
3.5%
T 120888
 
3.3%
Other values (31) 1514666
41.8%

Bank_Credits
Real number (ℝ)

High correlation  Missing 

Distinct2175
Distinct (%)1.8%
Missing20362
Missing (%)14.4%
Infinite0
Infinite (%)0.0%
Mean325334.82
Minimum5028.22
Maximum749550.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-06T16:54:10.326740image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum5028.22
5-th percentile32212.59
Q1100151.47
median205686.24
Q3604922.26
95-th percentile677149.72
Maximum749550.42
Range744522.2
Interquartile range (IQR)504770.79

Descriptive statistics

Standard deviation249719.87
Coefficient of variation (CV)0.76757805
Kurtosis-1.6789828
Mean325334.82
Median Absolute Deviation (MAD)157108.97
Skewness0.31218339
Sum3.9329076 × 1010
Variance6.2360012 × 1010
MonotonicityNot monotonic
2025-07-06T16:54:10.371514image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
749550.42 318
 
0.2%
652986.91 316
 
0.2%
697204.32 311
 
0.2%
681556.78 311
 
0.2%
709519.97 311
 
0.2%
651192.52 310
 
0.2%
684456.26 309
 
0.2%
695829.1 309
 
0.2%
678343.79 308
 
0.2%
684766.63 307
 
0.2%
Other values (2165) 117778
83.4%
(Missing) 20362
 
14.4%
ValueCountFrequency (%)
5028.22 4
< 0.1%
5239.3 6
< 0.1%
5700.37 7
< 0.1%
5851.66 3
 
< 0.1%
6105.74 5
< 0.1%
6537.55 4
< 0.1%
7265.99 4
< 0.1%
7395.72 9
< 0.1%
8551.64 4
< 0.1%
8736.6 6
< 0.1%
ValueCountFrequency (%)
749550.42 318
0.2%
711543.33 301
0.2%
709519.97 311
0.2%
708968.47 289
0.2%
701886.7 302
0.2%
701471.09 305
0.2%
700747.49 296
0.2%
697204.32 311
0.2%
696299.76 292
0.2%
695829.1 309
0.2%

Bank_Debits
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing20362
Missing (%)14.4%
Memory size7.1 MiB
0.0
120888 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters362664
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 120888
85.6%
(Missing) 20362
 
14.4%

Length

2025-07-06T16:54:10.412605image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:54:10.434142image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 120888
100.0%

Most occurring characters

ValueCountFrequency (%)
0 241776
66.7%
. 120888
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 362664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 241776
66.7%
. 120888
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 362664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 241776
66.7%
. 120888
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 362664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 241776
66.7%
. 120888
33.3%

end_to_end_recon_match
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size138.1 KiB
True
120888 
False
20362 
ValueCountFrequency (%)
True 120888
85.6%
False 20362
 
14.4%
2025-07-06T16:54:10.450009image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Interactions

2025-07-06T16:54:03.231516image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.069689image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.361160image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.661667image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.961354image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.244991image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.621577image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.931624image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:03.273724image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.105967image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.397630image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.697807image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.996018image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.280888image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.660357image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.967133image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:03.321759image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.144095image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.436357image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.737239image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.033260image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.318437image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.698849image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:03.004897image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:03.360670image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.178850image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.473786image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.772977image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.067504image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.355145image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.738077image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:03.042750image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:03.400382image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.214786image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.508955image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.808753image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.101651image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.390299image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.773892image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:03.079327image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:03.440594image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.250098image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.546978image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.845135image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.136853image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.427613image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.811811image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:03.115551image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:03.482581image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.287221image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.585470image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.885234image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.171961image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.543744image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.851603image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:03.154306image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:03.521975image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.322331image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.622307image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:01.922014image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.207995image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.581659image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:02.891097image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:54:03.190379image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Correlations

2025-07-06T16:54:10.484359image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
BS_AmountBS_Card_TypeBS_CurrencyBS_Foreign_Exchange_RateBS_GW_MatchBS_Payment_GatewayBS_Payment_Method_TypeBS_Processor_Response_MessageBS_Record_TypeBS_Sales_ChannelBank_CreditsBank_Deposit_CurrencyGW_Card_BrandGW_Net_Settled_AmtGW_Transaction_AmountGW_Trx_CurrencyGW_feesGW_nameMP_Processor_FeeMP_Processor_TypeMP_Settled_AmountMP_Trx_CurrencyPG_MP_Matchend_to_end_recon_match
BS_Amount1.0000.0030.004-0.0031.0000.0000.0000.5770.5770.0030.0120.0020.0031.0001.0000.0020.7600.0000.1260.0001.0000.0020.9560.840
BS_Card_Type0.0031.0000.0020.0020.0001.0001.0000.0000.0000.0000.4040.0001.0000.0060.0030.0000.3591.0000.5101.0000.0130.0000.0000.000
BS_Currency0.0040.0021.0001.0000.0000.0000.0000.0000.0000.0000.4891.0000.0000.0030.0041.0000.0000.0000.0000.0000.0031.0000.0000.000
BS_Foreign_Exchange_Rate-0.0030.0021.0001.0000.0000.0000.0000.0000.0000.000-0.0901.0000.000-0.007-0.0071.000-0.0040.000-0.0010.000-0.0061.0000.0000.000
BS_GW_Match1.0000.0000.0000.0001.0000.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9560.840
BS_Payment_Gateway0.0001.0000.0000.0000.0001.0001.0000.0000.0000.0000.5700.0001.0000.0070.0000.0000.5081.0000.6401.0000.0180.0000.0020.002
BS_Payment_Method_Type0.0001.0000.0000.0000.0001.0001.0000.0000.0000.0000.5700.0001.0000.0070.0000.0000.5081.0000.6401.0000.0180.0000.0020.002
BS_Processor_Response_Message0.5770.0000.0000.0001.0000.0000.0001.0001.0000.0001.0001.0000.0000.0000.0000.0000.0000.0011.0001.0001.0001.0001.0000.879
BS_Record_Type0.5770.0000.0000.0001.0000.0000.0001.0001.0000.0001.0001.0000.0000.0000.0000.0000.0000.0011.0001.0001.0001.0001.0000.879
BS_Sales_Channel0.0030.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0040.0040.0000.0040.0000.0040.0000.0040.0000.0000.000
Bank_Credits0.0120.4040.489-0.0901.0000.5700.5701.0001.0000.0001.0000.4890.4040.0130.0120.489-0.1660.570-0.4170.4660.0170.4891.0001.000
Bank_Deposit_Currency0.0020.0001.0001.0001.0000.0000.0001.0001.0000.0000.4891.0000.0000.0010.0021.0000.0000.0000.0000.0000.0021.0001.0001.000
GW_Card_Brand0.0031.0000.0000.0001.0001.0001.0000.0000.0000.0000.4040.0001.0000.0060.0030.0000.3591.0000.5101.0000.0130.0000.0000.002
GW_Net_Settled_Amt1.0000.0060.003-0.0071.0000.0070.0070.0000.0000.0040.0130.0010.0061.0001.0000.0030.7560.0070.1220.0061.0000.0030.0000.000
GW_Transaction_Amount1.0000.0030.004-0.0071.0000.0000.0000.0000.0000.0040.0120.0020.0031.0001.0000.0040.7600.0000.1260.0001.0000.0040.0000.000
GW_Trx_Currency0.0020.0001.0001.0001.0000.0000.0000.0000.0000.0000.4891.0000.0000.0030.0041.0000.0000.0000.0000.0000.0031.0000.0000.000
GW_fees0.7600.3590.000-0.0041.0000.5080.5080.0000.0000.004-0.1660.0000.3590.7560.7600.0001.0000.5080.5170.4150.7510.0000.0000.000
GW_name0.0001.0000.0000.0001.0001.0001.0000.0010.0010.0000.5700.0001.0000.0070.0000.0000.5081.0000.6401.0000.0180.0000.0010.004
MP_Processor_Fee0.1260.5100.000-0.0011.0000.6400.6401.0001.0000.004-0.4170.0000.5100.1220.1260.0000.5170.6401.0000.5890.1110.0001.0000.000
MP_Processor_Type0.0001.0000.0000.0001.0001.0001.0001.0001.0000.0000.4660.0001.0000.0060.0000.0000.4151.0000.5891.0000.0150.0001.0000.004
MP_Settled_Amount1.0000.0130.003-0.0061.0000.0180.0181.0001.0000.0040.0170.0020.0131.0001.0000.0030.7510.0180.1110.0151.0000.0031.0000.000
MP_Trx_Currency0.0020.0001.0001.0001.0000.0000.0001.0001.0000.0000.4891.0000.0000.0030.0041.0000.0000.0000.0000.0000.0031.0001.0000.000
PG_MP_Match0.9560.0000.0000.0000.9560.0020.0021.0001.0000.0001.0001.0000.0000.0000.0000.0000.0000.0011.0001.0001.0001.0001.0000.878
end_to_end_recon_match0.8400.0000.0000.0000.8400.0020.0020.8790.8790.0001.0001.0000.0020.0000.0000.0000.0000.0040.0000.0040.0000.0000.8781.000

Missing values

2025-07-06T16:54:03.678062image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-06T16:54:04.018360image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-06T16:54:04.768495image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

BS_Record_TypeBS_Transaction_DateBS_CurrencyBS_Customer_NumberBS_AmountBS_Payment_Method_TypeBS_Card_TypeBS_Processor_Transaction_IdBS_Payment_GatewayBS_Processor_Response_CodeBS_Processor_Response_MessageBS_Inv_Payment_NumberBS_Sales_ChannelBS_Foreign_Exchange_RateGW_Transaction_IDGW_nameGW_Transaction_StatusGW_Response_CodeGW_ResponseGW_Transaction_AmountGW_Trx_CurrencyGW_Trx_TypeGW_Card_BrandGW_feesGW_Net_Settled_AmtGW_Trx_DateGW_Ref_NumberBS_GW_MatchMP_Trx_IDMP_Processor_TypeMP_Trx_TypeMP_Processor_FeeMP_Settled_AmountMP_Settlement_DateMP_Settlement_BatchMP_Trx_CurrencyMP_Order_IDPG_MP_MatchBank_Deposit_DateBank_Deposit_CurrencyBank_Deposit_Ref_NumberBank_CreditsBank_Debitsend_to_end_recon_match
0payment2025-01-30USDCUST151592592.98PayPalPayPalPROC-21233551PayPal0ApprovedPAY-0000000In-Store1.0017GW-TRX-9972261PayPalsettled0Approved2592.98USDpaymentPayPal19.452573.532025-01-30PAY-0000000YesPROC-TRX-96148747PayPalpayment0.002573.532025-01-31SET-20250130-PayPal-N/A-USDUSDPAY-0000000Yes2025-02-03USDSET-20250130-PayPal-N/A-USD584871.770.0Yes
1payment2025-01-10USDCUST207794277.28ACHACHPROC-45142454GoCardless0ApprovedPAY-0000001In-Store1.0058GW-TRX-8455695GoCardlesssettled0Approved4277.28USDpaymentGoCardless10.694266.592025-01-10PAY-0000001YesPROC-TRX-53756634GoCardlesspayment0.004266.592025-01-13SET-20250110-GoCardless-N/A-USDUSDPAY-0000001Yes2025-01-14USDSET-20250110-GoCardless-N/A-USD621022.330.0Yes
2payment2025-02-21USDCUST284042515.78ACHACHPROC-76309118GoCardless0ApprovedPAY-0000002Phone1.0056GW-TRX-5184318GoCardlesssettled0Approved2515.78USDpaymentGoCardless6.292509.492025-02-21PAY-0000002YesPROC-TRX-96643401GoCardlesspayment0.002509.492025-02-24SET-20250221-GoCardless-N/A-USDUSDPAY-0000002Yes2025-02-25USDSET-20250221-GoCardless-N/A-USD655334.980.0Yes
3payment2025-01-04GBPCUST70072874.81Credit CardVisaPROC-62839998Cybersource0ApprovedPAY-0000003Phone1.2599GW-TRX-8104962Cybersourcesettled0Approved2874.81GBPpaymentVisa28.752846.062025-01-04PAY-0000003YesPROC-TRX-48503361ClientLinepayment28.752817.312025-01-07SET-20250104-ClientLine-Visa-GBPGBPPAY-0000003Yes2025-01-08GBPSET-20250104-ClientLine-Visa-GBP38585.640.0Yes
4payment2025-03-16USDCUST39746350.01PayPalPayPalPROC-62414033PayPal0ApprovedPAY-0000004In-Store1.0060GW-TRX-2770667PayPalsettled0Approved350.01USDpaymentPayPal2.63347.382025-03-16PAY-0000004YesPROC-TRX-87613870PayPalpayment0.00347.382025-03-19SET-20250316-PayPal-N/A-USDUSDPAY-0000004Yes2025-03-20USDSET-20250316-PayPal-N/A-USD681556.780.0Yes
5payment2025-02-10USDCUST30554412.91Credit CardMastercardPROC-58248354Cybersource0ApprovedPAY-0000005Online1.0076GW-TRX-8558984Cybersourcesettled0Approved4412.91USDpaymentMastercard44.134368.782025-02-10PAY-0000005YesPROC-TRX-40189275ClientLinepayment44.134324.652025-02-11SET-20250210-ClientLine-Mastercard-USDUSDPAY-0000005Yes2025-02-12USDSET-20250210-ClientLine-Mastercard-USD138689.960.0Yes
6payment2025-02-07AUDCUST6360399.15ACHACHPROC-58711045GoCardless0ApprovedPAY-0000006In-Store0.6555GW-TRX-5053867GoCardlesssettled0Approved399.15AUDpaymentGoCardless1.00398.152025-02-07PAY-0000006YesPROC-TRX-13724055GoCardlesspayment0.00398.152025-02-10SET-20250207-GoCardless-N/A-AUDAUDPAY-0000006Yes2025-02-11AUDSET-20250207-GoCardless-N/A-AUD89870.350.0Yes
7payment2025-02-20GBPCUST448561639.12ACHACHPROC-99846281GoCardless0ApprovedPAY-0000007In-Store1.2759GW-TRX-1689560GoCardlesssettled0Approved1639.12GBPpaymentGoCardless4.101635.022025-02-20PAY-0000007YesPROC-TRX-57320369GoCardlesspayment0.001635.022025-02-21SET-20250220-GoCardless-N/A-GBPGBPPAY-0000007Yes2025-02-24GBPSET-20250220-GoCardless-N/A-GBP71271.860.0Yes
8payment2025-02-25USDCUST8902802.45Credit CardAmexPROC-65593396Cybersource0ApprovedPAY-0000008Online1.0027GW-TRX-5187601Cybersourcesettled0Approved802.45USDpaymentAmex8.02794.432025-02-25PAY-0000008YesPROC-TRX-36380772Amexpayment12.04782.392025-02-26SET-20250225-Amex-Amex-USDUSDPAY-0000008Yes2025-02-27USDSET-20250225-Amex-Amex-USD235013.680.0Yes
9payment2025-02-23USDCUST168874780.71Credit CardMastercardPROC-56406364Cybersource0ApprovedPAY-0000009Online0.9977GW-TRX-8963563Cybersourcesettled0Approved4780.71USDpaymentMastercard47.814732.902025-02-23PAY-0000009YesPROC-TRX-55314596ClientLinepayment47.814685.092025-02-26SET-20250223-ClientLine-Mastercard-USDUSDPAY-0000009Yes2025-02-27USDSET-20250223-ClientLine-Mastercard-USD178556.960.0Yes
BS_Record_TypeBS_Transaction_DateBS_CurrencyBS_Customer_NumberBS_AmountBS_Payment_Method_TypeBS_Card_TypeBS_Processor_Transaction_IdBS_Payment_GatewayBS_Processor_Response_CodeBS_Processor_Response_MessageBS_Inv_Payment_NumberBS_Sales_ChannelBS_Foreign_Exchange_RateGW_Transaction_IDGW_nameGW_Transaction_StatusGW_Response_CodeGW_ResponseGW_Transaction_AmountGW_Trx_CurrencyGW_Trx_TypeGW_Card_BrandGW_feesGW_Net_Settled_AmtGW_Trx_DateGW_Ref_NumberBS_GW_MatchMP_Trx_IDMP_Processor_TypeMP_Trx_TypeMP_Processor_FeeMP_Settled_AmountMP_Settlement_DateMP_Settlement_BatchMP_Trx_CurrencyMP_Order_IDPG_MP_MatchBank_Deposit_DateBank_Deposit_CurrencyBank_Deposit_Ref_NumberBank_CreditsBank_Debitsend_to_end_recon_match
141240chargeback_reversal2025-03-26USDCUST35141689.24ACHACHPROC-27835908GoCardlessC02Chargeback ReversedPAY-0107910In-Store0.9972GW-TRX-2559292GoCardlesssettled00Approved689.24USDpaymentGoCardless1.72687.522025-03-26PAY-0107910YesNaNNaNNaNNaNNaNNaNNaNNaNNaNNoNaNNaNNaNNaNNaNNo
141241chargeback_reversal2025-03-06USDCUST386152318.44Credit CardMastercardPROC-12729812CybersourceC02Chargeback ReversedPAY-0024103In-Store0.9920GW-TRX-7827390Cybersourcesettled0Approved2318.44USDpaymentMastercard23.182295.262025-03-06PAY-0024103YesNaNNaNNaNNaNNaNNaNNaNNaNNaNNoNaNNaNNaNNaNNaNNo
141242chargeback_reversal2025-03-11CADCUST330124353.13Credit CardAmexPROC-47353736CybersourceC02Chargeback ReversedPAY-0039969In-Store0.7286GW-TRX-4868451Cybersourcesettled0Approved4353.13CADpaymentAmex43.534309.602025-03-11PAY-0039969YesNaNNaNNaNNaNNaNNaNNaNNaNNaNNoNaNNaNNaNNaNNaNNo
141243chargeback_reversal2025-01-31GBPCUST2991628.61PayPalPayPalPROC-65209114PayPalC02Chargeback ReversedPAY-0101060Online1.2621GW-TRX-2579080PayPalsettled00Approved28.61GBPpaymentPayPal0.2128.402025-01-31PAY-0101060YesNaNNaNNaNNaNNaNNaNNaNNaNNaNNoNaNNaNNaNNaNNaNNo
141244chargeback_reversal2025-01-24USDCUST207532263.30ACHACHPROC-90546965GoCardlessC02Chargeback ReversedPAY-0020178Online0.9962GW-TRX-6667190GoCardlesssettled0Approved2263.30USDpaymentGoCardless5.662257.642025-01-24PAY-0020178YesNaNNaNNaNNaNNaNNaNNaNNaNNaNNoNaNNaNNaNNaNNaNNo
141245chargeback_reversal2025-03-12USDCUST169692573.44ACHACHPROC-65330212GoCardlessC02Chargeback ReversedPAY-0025245Online0.9928GW-TRX-4965129GoCardlesssettled0Approved2573.44USDpaymentGoCardless6.432567.012025-03-12PAY-0025245YesNaNNaNNaNNaNNaNNaNNaNNaNNaNNoNaNNaNNaNNaNNaNNo
141246chargeback_reversal2025-01-01EURCUST33287794.03Credit CardAmexPROC-79034160CybersourceC02Chargeback ReversedPAY-0093761Online1.0854GW-TRX-9449979Cybersourcesettled0Approved794.03EURpaymentAmex7.94786.092025-01-01PAY-0093761YesNaNNaNNaNNaNNaNNaNNaNNaNNaNNoNaNNaNNaNNaNNaNNo
141247chargeback_reversal2025-02-13USDCUST121123629.53PayPalPayPalPROC-53163760PayPalC02Chargeback ReversedPAY-0103102Online0.9919GW-TRX-7642246PayPalsettled00Approved3629.53USDpaymentPayPal27.223602.312025-02-13PAY-0103102YesNaNNaNNaNNaNNaNNaNNaNNaNNaNNoNaNNaNNaNNaNNaNNo
141248chargeback_reversal2025-02-10USDCUST274872700.28Credit CardMastercardPROC-29829469CybersourceC02Chargeback ReversedPAY-0039089Phone0.9963GW-TRX-5426672Cybersourcesettled0Approved2700.28USDpaymentMastercard27.002673.282025-02-10PAY-0039089YesNaNNaNNaNNaNNaNNaNNaNNaNNaNNoNaNNaNNaNNaNNaNNo
141249chargeback_reversal2025-01-05CADCUST233982500.12ACHACHPROC-69324390GoCardlessC02Chargeback ReversedPAY-0022422Online0.7254GW-TRX-9447663GoCardlesssettled0Approved2500.12CADpaymentGoCardless6.252493.872025-01-05PAY-0022422YesNaNNaNNaNNaNNaNNaNNaNNaNNaNNoNaNNaNNaNNaNNaNNo